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Fusion: Practice and Applications

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Fusion: Practice and Applications

Volume 15 , Issue 2 , PP: 173-186, 2024 | Cite this article as | XML | Html | PDF | Full Length Article

Exploring Advanced Techniques in Multilevel Fusion Score Level for Enhanced Data Integration in Complex Systems

Tri Rijanto 1 * , B. Santhosh Kumar 2 , Aws Zuhair Sameen 3 , Takveer Singh 4 , Suruchi Pimple 5 , Swati M. Patil 6

  • 1 Electrical engineering, Universitas Negeri Surabaya, Indonesia - (tririjanto@unesa.ac.id)
  • 2 Computer science and Engineering, G Pulla Reddy Engineering College, India - (santhoshkumar.bala@gmail.com)
  • 3 College of Medical Techniques, Al-Farahidi University, Baghdad, Iraq - (aws.zuhair@uoalfarahidi.edu.iq)
  • 4 Centre for Interdisciplinary Research in Business and Technology, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India - (takveer.singh.orp@chitkara.edu.in)
  • 5 Asst.Professor Computer Science Department College Sadabai Raisoni Women's College, Nagpur, Maharashtra, India - (suruchi.pimple@raisoni.net)
  • 6 Assistant professor College name: Rajarambapu Institute of Technology, Islampur, ( An Autonomous Institute Affiliated to shivaji University, Kolhapur), India - (swatim.patil@ritindia.edu)

    • Doi: https://doi.org/10.54216/FPA.150216

    Received: August 22, 2023 Revised: December 05, 2023 Accepted: April 13, 2024
    Abstract

    We have discovered five novel strategies to enhance data fusion in complex systems. This page provides a comprehensive explanation of these five methodologies. Data may be combined with a list. Examples of techniques include entropy-based data selection and parameter optimization for data fusion. This technique effectively resolves all problems related to merging records. Accurate, rapid, and easily expandable. Ablation studies assess the effectiveness of various techniques. Every process is crucial; omitting anyone would adversely affect the mix. This approach may integrate data from several sources to guarantee accuracy and utility. This facilitates the use of intricate technologies, hence enhancing data integration. The study promotes further inquiry and implementation. These results indicate that using this method might enhance the process of combining data.

    Keywords :

    Anomaly Detection , Data Integration , Data Scalability , Entropy-Based Selection , Fusion Algorithms , Multilevel Integration , Parameter Optimization , Precision , Robustness.

    References

    [1]  Karandikar J (2019) Machine learning classification for tool life modeling using production shop-floor tool wear data. Procedia Manufacturing 34:446–454

    [2]  Abbass J, Al-Habaibeh A (2015) A comparative study of using spindle motor power and eddy current for the detection of tool conditions in milling processes. IEEE 13th International Conference on Industrial Informatics (INDIN). pp 766–770

    [3]  D. Pathak and R. Kashyap, "Neural correlate-based E-learning validation and classification using convolutional and Long Short-Term Memory networks," Traitement du Signal, vol. 40, no. 4, pp. 1457-1467, 2023. [Online]. Available: https://doi.org/10.18280/ts.400414

    [4]  Debie, E.; Fernandez Rojas, R.; Fidock, J.; Barlow, M.; Kasmarik, K.; Anavatti, S.; Garratt, M.; Abbass, H.A. Multimodal Fusion for Objective Assessment of Cognitive Workload: A Review. IEEE Trans. Cybern. 2021, 51, 1542–1555. [Google Scholar] [CrossRef]

    [5]  Liu, R.; Yang, B.; Zio, E.; Chen, X. Artificial Intelligence for Fault Diagnosis of Rotating Machinery: A Review. Mech. Syst. Signal Process. 2018, 108, 33–47.

    [6]  Peng, B.; Bi, Y.; Xue, B.; Zhang, M.; Wan, S. A Survey on Fault Diagnosis of Rolling Bearings. Algorithms 2022, 15, 347.

    [7]  Hoang, D.-T.; Kang, H.-J. A Survey on Deep Learning Based Bearing Fault Diagnosis. Neurocomputing 2019, 335, 327–335.

    [8]  Zhang, S.; Zhang, S.; Wang, B.; Habetler, T.G. Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review. IEEE Access 2020, 8, 29857–29881.

    [9]  N. Ben-Asher and C. Gonzalez, "Effects of cyber security knowledge on attack detection," Computers in Human Behavior, vol. 48, pp. 51–61, 2015.

    [10]   Huang, J. Research and Application of Filtering Method for LiDAR Point Cloud and Image Matching Point Cloud; China University of Mining and Technology: Xuzhou, China, 2022.

    [11]   V. Roy et al., “Detection of sleep apnea through heart rate signal using Convolutional Neural Network,” International Journal of Pharmaceutical Research, vol. 12, no. 4, pp. 4829-4836, Oct-Dec 2020.

    [12]   Zhang X, Zhang Q, Chen M, Sun Y, Qin X, Li H (2018) A two-stage feature selection and intelligent fault diagnosis method for rotating machinery using hybrid filter and wrapper method. Neurocomputing 275:2426–2439

    [13]   D. Putra and A. Wibowo, "Sentiment Analysis for Board Game Review using Deep Learning and Sentiment Lexicon," Int. J. Emerg. Technol. Adv. Eng., vol. 12, no. 6, pp. 56–62, 2022.

    [14]   Dlesk, A. Documentation of Historical Underground Object in Skorkov Village with Selected Measuring Methods, Data Analysis and Visualization. ISPRS—Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2016, XLI-B5, 251–254.

    [15]   Ercoli, M.; Brigante, R.; Radicioni, F.; Pauselli, C.; Mazzocca, M.; Centi, G.; Stoppini, A. Inside the polygonal walls of Amelia (Central Italy): A multidisciplinary data integration, encompassing geodetic monitoring and geophysical prospections. J. Appl. Geophys. 2016, 127, 31–44.

    [16]   Cowie, P.; Phillips, R.; Roberts, G.; Mccaffrey, K.; Wilkinson, M. Orogen-scale uplift drives episodic behaviour of earthquake faults. Sci. Rep. 2017, 7, 44858.

    [17]   Zhang, D.; Wu, Z.; Shi, D.; Li, J.; Lu, Y. Integration of Terrestrial Laser Scanner (TLS) and Ground Penetrating Radar (GPR) to Characterize the Three-Dimensional (3D) Geometry of the Maoyaba Segment of the Litang Fault, Southeastern Tibetan Plateau. Remote Sens. 2022, 14, 6394.

    [18]   Q. A. Ali, H. S. Alhares, H. H. Abd‐almohi, M. J. M‐Ridha, S. J. Mohammed, et al., "Enhancing Microbial Desalination Cell Performance for Water Desalination and Wastewater Treatment: Experimental Study and Modelling of Electrical Energy Production in Open and …," J. Chem. Technol. Biotechnol., 2024.

    [19]   M. K. Sharma, Nitesh Dhiman, Shubham Kumar, Laxmi Rathour, Vishnu Narayan Mishra, Neutrosophic Monte Carlo Simulation Approach for Decision Making In Medical Diagnostic Process Under Uncertain Environment, Journal of International Journal of Neutrosophic Science, Vol. 22 , No. 1 , (2023) : 08-16 (Doi   :  https://doi.org/10.54216/IJNS.220101)

    [20]   Rozin Majeed Abdullah, Adnan Mohsin Abdulazeez, Electrocardiogram Classification Based on Deep Convolutional Neural Networks: A Review, Journal of Fusion: Practice and Applications, Vol. 3 , No. 1 , (2021) : 43-53 (Doi   :  https://doi.org/10.54216/FPA.030103)

    [21]   Lee, W.J.; Wu, H.; Huang, A.; Sutherland, J.W. Learning via Acceleration Spectrograms of a DC Motor System with Application to Condition Monitoring. Int. J. Adv. Manuf. Technol. 2020, 106, 803–816.

    [22]   Chen, Z.; Li, W. Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network. IEEE Trans. Instrum. Meas. 2017, 66, 1693–1702.

    [23]   Nasir, V.; Dibaji, S.; Alaswad, K.; Cool, J. Tool Wear Monitoring by Ensemble Learning and Sensor Fusion Using Power, Sound, Vibration, and AE Signals. Manuf. Lett. 2021, 30, 32–38.

    [24]   Gültekin, Ö.; Çinar, E.; Özkan, K.; Yazıcı, A. A Novel Deep Learning Approach for Intelligent Fault Diagnosis Applications Based on Time-Frequency Images. Neural Comput. Appl. 2022, 34, 4803–4812.

    [25]   S. Stalin, V. Roy, P. K. Shukla, A. Zaguia, M. M. Khan, P. K. Shukla, A. Jain, "A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach," Mathematical Problems in Engineering, vol. 2021, Article ID 2942808, 11 pages, 2021. [Online]. Available: https://doi.org/10.1155/2021/2942808

    [26]  V. Roy et al., "Detection of sleep apnea through heart rate signal using Convolutional Neural Network," International Journal of Pharmaceutical Research, vol. 12, no. 4, pp. 4829-4836

    Cite This Article As :
    Rijanto, Tri. , Santhosh, B.. , Zuhair, Aws. , Singh, Takveer. , Pimple, Suruchi. , M., Swati. Exploring Advanced Techniques in Multilevel Fusion Score Level for Enhanced Data Integration in Complex Systems. Fusion: Practice and Applications, vol. , no. , 2024, pp. 173-186. DOI: https://doi.org/10.54216/FPA.150216
    Rijanto, T. Santhosh, B. Zuhair, A. Singh, T. Pimple, S. M., S. (2024). Exploring Advanced Techniques in Multilevel Fusion Score Level for Enhanced Data Integration in Complex Systems. Fusion: Practice and Applications, (), 173-186. DOI: https://doi.org/10.54216/FPA.150216
    Rijanto, Tri. Santhosh, B.. Zuhair, Aws. Singh, Takveer. Pimple, Suruchi. M., Swati. Exploring Advanced Techniques in Multilevel Fusion Score Level for Enhanced Data Integration in Complex Systems. Fusion: Practice and Applications , no. (2024): 173-186. DOI: https://doi.org/10.54216/FPA.150216
    Rijanto, T. , Santhosh, B. , Zuhair, A. , Singh, T. , Pimple, S. , M., S. (2024) . Exploring Advanced Techniques in Multilevel Fusion Score Level for Enhanced Data Integration in Complex Systems. Fusion: Practice and Applications , () , 173-186 . DOI: https://doi.org/10.54216/FPA.150216
    Rijanto T. , Santhosh B. , Zuhair A. , Singh T. , Pimple S. , M. S. [2024]. Exploring Advanced Techniques in Multilevel Fusion Score Level for Enhanced Data Integration in Complex Systems. Fusion: Practice and Applications. (): 173-186. DOI: https://doi.org/10.54216/FPA.150216
    Rijanto, T. Santhosh, B. Zuhair, A. Singh, T. Pimple, S. M., S. "Exploring Advanced Techniques in Multilevel Fusion Score Level for Enhanced Data Integration in Complex Systems," Fusion: Practice and Applications, vol. , no. , pp. 173-186, 2024. DOI: https://doi.org/10.54216/FPA.150216

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